System and method of applying power laws in optimizing network traffic

ABSTRACT

A system and method of predicting network data traffic includes coupling a first group of clients to a current server that results in a current CPU utilization of the current server. A second group of clients are coupled to the current server. A load multiple is determined and the current CPU utilization is compared to a predicted CPU utilization. A server requirement is increased if the current CPU utilization is greater than or equal to the predicted CPU utilization.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates generally to systems and methodsfor managing data networks, and more particularly, to methods andsystems for managing network data traffic.

[0003] 2. Description of the Related Art

[0004] Computer networks are very common. Computer networks can be smalllocal area networks (LAN) connecting only a few computers or can be widearea networks (WAN) connecting an entire enterprise of multiple LANs.The Internet can also be considered as a very large WAN. As the numberof computers and users on the network increases then the network datatraffic will also increase. As the network data traffic increases then“choke” points can develop at a network node where data flow isrestricted due to some shortfall in that node. The choke point slowsdown the data traffic. For example, a server is used to serve data torequesting clients via the computer network that interconnects theserver and the clients. As the number of clients increases, the volumeof data being served by the server also increases. Eventually, thevolume of data being requested by the clients becomes greater than thevolume of data the server can serve in a timely manner. As a result, theserver delays sending the requested data and the data throughput of thenetwork is choked by the limited data throughput capability of theserver. Similarly, any other node can become a choke point when theoutput demands on the node become greater than the output capabilitiesof the node. A properly designed network minimizes choke points so as tomaximize data flow. In addition an accurate understanding of networkdata traffic allows the network data traffic to be distributed moreevenly across servers, routers and other network nodes.

[0005] Traditionally, network traffic predictions have been based on aPoisson distribution pattern. A Poisson distribution is a probabilitydensity function that is often used as a mathematical model of thenumber of outcomes obtained in a suitable interval of time and space. Apoison distribution has its mean equal to its variance, that is used asan approximation to the binomial distribution, and that has the form of:

f(x)=e ^(−μ)μ^(x) /x!  Formula 1

[0006] where μ is the mean and x takes on nonnegative integral values.

[0007]FIGS. 1A, 1B and 1C show a typical Poisson distribution pattern ofdata traffic 100 in a network at three different time bases. FIG. 1Ashows the data traffic 100 at a one hundred second time base. The datashown appears to be approximately uniform in density (measured inpackets/unit time on the Y-axis) and frequency (on the X-axis) thereforeresulting in a generally uniform appearing graph with no significantpeaks or valleys.

[0008]FIG. 1B shows the same data traffic 100 with a time base of onesecond having approximately the same pattern. Again the data trafficshown in FIG. 1B appears to be approximately uniform in density andfrequency therefore resulting in a uniform appearing graph but with whatappears to be very minor peaks and valleys that are very closely spaced.

[0009]FIG. 1C shows the same data traffic 100 with a time base of 0.01second that shows some periodic variations in the data distribution suchas periodic peaks 110A, 110B and periodic valleys 112A, 112B.

[0010] In sum, a Poisson distribution appears approximately uniform at alarge time base (e.g., one hundred seconds) with some periodic peaks andvalleys at a relatively small time base (e.g., 0.01 seconds).

[0011] When a network is being designed, a Poisson pattern istraditionally used to model the predicted data traffic in the network.The Poisson model has also been used when managing and operatingnetworks such as to determine optimum times for back-up and networkinterruption for repairs or identify network nodes needing improvementso as to avoid a choke point developing. An accurate data trafficpattern projection can also provide insight into other aspects of thenetwork operations such as load balancing and other operations.

[0012] However, actual studies of actual network data traffic show thedata traffic actually follows a pattern that has peaks and valleys atany time base rather than a Poisson pattern as shown in FIGS. 1A-Cabove. For example, one study by Will E. Leland, Murad S. Taqqu, WalterWillinger, and Daniel V. Wilson, and entitled “On the Self-SimilarNature of Ethernet Traffic (Extended Version)”, IEEE/ACM Trans.Networking, Vol. 2., pp. 1-15, January 1994 (hereafter referred to asLeland) is incorporated by reference herein in its entirety for allpurposes. Leland examined data packet traffic flow in an Ethernet LAN.

[0013]FIGS. 2A and 2B show a graph 200 of the data packet traffic flowthat Leland actually measured at different time bases. FIG. 2A shows thegraph 200 with a time base of one hundred seconds. Even at a one hundredsecond time base, significant peaks 210A, 210B, 210C, 210D and valleys212A, 212B, 212C, 212D are evident. As the time base is decreased to0.01 seconds, in FIG. 2B, significant peaks 210E, 210F, 210G, 210H andvalleys 212E, 212F, 212G, 212H are also shown. Because the presence ofpeaks 210A-H and valleys 212A-H are constant, regardless of the timebase, the patterns can also be said to be self-similar in that they haveapproximately the same form regardless of time base.

[0014] Another study of interest is by Vern Paxson and Sally Floyd, andentitled “Wide Area Traffic: The Failure of Poisson Modeling”, IEEE/ACMTrans. Networking, Vol. 3, pp. 226-244, June 1995 (hereafter referred toas Paxson) which is incorporated by reference herein in its entirety forall purposes. Paxson examined WAN network traffic. Paxson also foundthat Poisson was not sufficiently accurate model of packet datatransfer, which makes up the bulk of WAN data traffic. Paxson alsoidentified a bursty (i.e., having peaks and valleys), self-similarpattern to the packet data transfer through the WAN which is similar tothe data traffic flow wave forms found by Leland above.

[0015] A study of Internet packet data traffic by Mark E. Crovella andAzer Bestavros, and entitled “Self-Similarity in WWW traffic: Evidenceand Possible Causes,” IEEE/ACM Trans. Networking, Vol. 5, pp 835-846,December 1997, (Crovella), is incorporated by reference herein in itsentirety for all purposes. Crovella found that packet data traffic onthe world wide web also followed a bursty, self-similar pattern and nota Poisson pattern.

[0016] Each of the studies (Leland, Paxson and Crovella) showed thatPoisson models do not accurately represent or predict actual packet dataflow patterns. In view of the foregoing, there is a need for a systemand method of more accurately predicting network data traffic.

SUMMARY OF THE INVENTION

[0017] Broadly speaking, the present invention fills these needs byproviding a system and method for predicting and managing network datatraffic. It should be appreciated that the present invention can beimplemented in numerous ways, including as a process, an apparatus, asystem, computer readable media, or a device. Several inventiveembodiments of the present invention are described below.

[0018] One embodiment provides a system and method of predicting networkdata traffic includes coupling a first group of clients to a currentserver that results in a current CPU utilization of the current server.A second group of clients are coupled to the current server. A loadmultiple is determined and the current CPU utilization is compared to apredicted CPU utilization. A server requirement is increased if thecurrent CPU utilization is greater than or equal to the predicted CPUutilization.

[0019] The load multiple can be equal to a sum of the first plurality ofclient nodes and the second plurality of client nodes divided by thefirst plurality of client nodes.

[0020] The predicted CPU utilization can be equal to an inverse of aproduct of the first plurality of client nodes and the load multiple toa scaling exponent. The scaling exponent can be equal to a multiple ofabout ⅓. The scaling exponent can be equal to about ⅓.

[0021] Increasing the server requirement can include adding additionalserver CPU capacity. Adding additional server CPU capacity can includeadding additional server CPU capacity until the current CPU utilizationis greater than the predicted CPU utilization.

[0022] Coupling a first group of clients to the current server caninclude receiving a first group of requests from the first group ofclients. Coupling a second group of clients to the current server caninclude receiving a second group of requests from the second group ofclients.

[0023] Increasing the server requirement can include outputting thepredicted CPU utilization.

[0024] Another embodiment includes a method of predicting network datatraffic that includes coupling a first group of client nodes to acurrent server and coupling a second group of client nodes to thecurrent server. A load multiple is determined. The load multiple isequal to a sum of the first group of client nodes and the second groupof client nodes divided by the first group of client nodes. A currentCPU utilization of the current server is compared to a predicted CPUutilization. The predicted CPU utilization is equal to an inverse of aproduct of the first group of client nodes and the load multiple to a ⅓exponent. A server requirement is increased if the current CPUutilization is greater than or equal to the predicted CPU utilization.

[0025] Another embodiment includes a system for managing network datatraffic and includes a server system that is coupled to a computernetwork. A first group of clients are coupled to the network. A secondgroup of clients are also coupled to the network. A load managing deviceis coupled to the network. The load managing device includes logic thatdetermines a current CPU utilization of the server system and logic thatdetermines a load multiple. The load managing device also includes logicthat compares the current CPU utilization to a predicted CPU utilizationand logic that increases a server requirement if the current CPUutilization is greater than or equal to the predicted CPU utilization.

[0026] The logic that increases the server requirement can include logicthat adds additional server CPU capacity. The logic that increases theserver requirement can include logic that couples at least oneadditional server to the network. The logic that adds additional serverCPU capacity can also include logic that adds additional server CPUcapacity until the current CPU utilization is greater than the predictedCPU utilization.

[0027] The load managing device can also include logic that receives afirst group of requests from the first group of clients and logic thatreceives a group plurality of requests from the second group of clientnodes.

[0028] The logic that increases the server requirement can also includelogic that outputs the predicted CPU utilization.

[0029] The present invention provides the advantage of a more accurateprediction or model of data traffic than prior art methods. Theincreased accuracy can provide improved network management capabilitiesand thereby enhance network planning, performance and design.

[0030] Other aspects and advantages of the invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be readily understood by the followingdetailed description in conjunction with the accompanying drawings, andlike reference numerals designate like structural elements.

[0032]FIG. 1A shows the data traffic at a one hundred second time base.

[0033]FIG. 1B shows the same data traffic as shown in FIG. 1A with atime base of one second having approximately the same pattern.

[0034]FIG. 1C shows the same data traffic as shown in FIGS. 1A and 1Babove, with a time base of 0.01 second.

[0035]FIG. 2A shows the graph of another set of data traffic with a timebase of one hundred seconds.

[0036]FIG. 2B shows the graph the data traffic shown in FIG. 2A with atime base of 0.01 seconds.

[0037]FIG. 3 shows a computer local area network (LAN) according to oneembodiment of the present invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0038] Several exemplary embodiments of systems and methods ofpredicting network data traffic using a power law will now be described.It will be apparent to those skilled in the art that the presentinvention may be practiced without some or all of the specific detailsset forth herein.

[0039] Prior art network traffic models are based upon a Poissondistribution theory. As described above in FIGS. 1A-2B, several studieshave found that the Poisson distribution-based model is not an accuraterepresentation of actual network data flow. Actual network data flowtraffic patterns were bursty and self-similar rather than Poisson-likedistribution. Self-similarity can indicate a long-range correlation.Long-range correlations can often be described by a power law-typerelationship. One embodiment includes a precise power law model that canbe used to accurately represent network data traffic.

[0040]FIG. 3 shows a computer local area network (LAN) 300 according toone embodiment of the present invention. The LAN 300 includes a network310 that couples the workstations (i.e., clients) 302A-n and the servers304A to 304 n. Additional servers and workstations may also be used andthe network 310 may also include connection to a larger WAN and/or theInternet. The network can also include intermediary points such as hubs,switches, routers and other nodes.

[0041] For purposes of modeling data traffic, each workstation 302A-ncan be said to demand approximately the same amount of data via thenetwork 310. Therefore the data demands of each workstation 302A-n canbe described as being invariant.

[0042] The statistical study of a mechanical system, near a phasetransition shows several interesting features. First, the degrees offreedom change in number and nature. Second phase transitions of secondkind discontinuities are observed in extensive quantities. Third,renormalization schemes can be developed to compute observable behavior.Fourth, there are universal critical exponents that define the behaviorof the system at criticality.

[0043] As the system approaches criticality, the system displayslong-range correlations, as well as self-similarity. In view of thesecond feature, the observable change their functional dependence from afalling exponent to a power law. The existence of critical exponent of auniversal nature indicates the power law that describes the system canhave a relatively simple form.

[0044] In a study of complex biological network systems by Geoffrey B.West, James H. Brown, Brian J. Enquist, entitled “A General Model ofAllometric Scaling in Biology,” Science, Vol. 276, p. 122, 1997 (West)found that many such biological supply systems (e.g., a blood stream)have an allometric scaling. West's study found that a biologicalvariable Y depends on the body mass M in an allometric scalingrelationship described by the following formula:

Y=cM^(β)  Formula 2

[0045] Where β is a scaling exponent and c is a constant that depends oncharacteristics of then particular organism. If this relationshipreflected geometric constraints then β is a simple multiple of ⅓.However, West found that in biological systems the power (i.e.,exponent) β is a simple multiple of ¼.

[0046] West illustrates that a power law relationship having theexponent β is a simple multiple of ¼, if three simple assumptions aretaken: First, that a space-filling fractal branching pattern is requiredfor the system to supply the entire volume of the organism. Second, thefinal branch of the system is a size-invariant unit. Third, the energyrequired to distribute the resources is minimized. Following West'sanalyses, is typical lengths l_(k) and radii r_(k) for the branches ofthe system, the volume rate of flow is defined by the following Formula3:

(δQ ₀)/(δt)=πr ² _(k) u _(k)   Formula 3

[0047] Where u_(k) is the average flow velocity. Each level k has nkbranches, so at each level the total number of branches is N_(k)=n₀n₁, .. . n_(k). The terminal units are size invariant and are defined byradius r_(c), length l_(c) and average flow velocity u_(c) and numberN_(c). West further shows that:

(δQ ₀)/(δt)=B=M ^(a) =N _(k)(δQ ₀)/(δt)=N _(k) πr ² _(k) u _(k) =r ²_(c) u _(c)   Formula 4

[0048] So that N_(c)=M^(a). West then introduced scale factorsδ_(k)=(r_(k+1))/r_(k) and γ_(k)=(l_(k+1))/l_(k). For self-similarfractals, δ_(k)=δ, γ_(k)=γ and N_(k)=n^(k), so N_(c)=n^(n).

[0049] The behavior of total volume V_(b) in the biological system canbe characterized by the following relationships:

V _(b)=Σ_(k) N _(k) V _(k)=Σ_(k) πr _(k) ² l _(k) n _(k) =N _(c) πr ²_(c) u _(c)   Formula 5

V _(b) =V ₀/(1−nγδ ²)=V _(c)(γδ²)^(−N)/(1−nγδ ²)   Formula 6

[0050] Since it can be shown that V_(b)=M, therefore (γδ²)^(−N)=M anda=−ln(n)/ln(γδ²).

[0051] The existence of a space filling system implies that the size ofthe system can be described by l_(k)>>r_(k) and a volume of the k^(th)level is equal to {fraction (4/3)}π(l_(k)/2)³N_(k).

[0052] Energy minimization can imply that the flow is volume preservingor area preserving. Volume preserving for space filling system providesthe following relationship:

{fraction (4/3)}π(l_(k)/2)³ N _(k)={fraction (4/3)}π(l_(k+1)/2)³N_(k+1)  Formula 7

[0053] Further, since (γ_(k))³={(l_(k+1))/l_(k}) ³=N_(k)/N_(k+1)=1/n,therefore γ_(k)=n^(−1/3)=γ.

[0054] However, an area preserving flow has a different implication.Because πr² _(k)=n πr² _(k+1) or δ_(k)=(r_(k+1))/r_(k)=n^(−1/2)=δ,γ=n^(−1/2). This implies with γ=n^(−1/3), that a={fraction (3/4)} andB=M^(3/4), r₀=M^(3/8), l₀=M^(1/4).

[0055] According to West's theory, biological systems are areapreserving and therefore the biological system scales with multiples of¼.

[0056] A computer network (such as computer network 300) is a network ofservers, clients, switches, routers and hubs with ports p_(k). Signalsare transported by this network from one node to another node. To supplyall of the nodes with signal, the network can be described as having afractal branching pattern. The fractal branching of the network 300 canbe characterized as being linear preserving of nodes or hops h_(k),meaning that the data travels through the least number of nodes andhops. Each of the final branches of the network 300 (e.g., workstations302A-302 n, or other end port) has substantially the same datathroughput and therefore the energy required to distribute the datatraffic on the network 300 is minimized.

[0057] The existence of a fractal pattern implies that the network 300can be described by the relationship of h_(k)>>p_(k). The fractalpattern is linear preserving in nodes (somewhat analogous to areapreserving in a biological system above) and the total number of nodesat the k^(th) level is p_(k)N_(k). Therefore: p_(k)N_(k)=p_(k+1)N_(k+1)and γ_(k)=(l_(k+1))/l_(k)=N_(k)/N_(k+1)=1/n, therefore γ_(k)=n⁻¹=γ.

[0058] Line preserving flow also minimizes the energy required todistribute the data signal implies for hops h_(k)=nh_(k+1), orδ_(k)=(h_(k+1))/h_(k)=n⁻¹=δ. When combined with γ=n⁻¹ from above, thisprovides a relationship of a=⅓ and Y=N^(1/3)p₀=N^(1/3), h₀=N^(1/3).

[0059] Thus, a quantity Y depends on the number of hosts in the networkproportional to the ⅓ power of those nodes. Therefore, in oneembodiment, the average amount of traffic at a given node p₀ isproportional to a similar quantity of the total number of nodes to the ⅓power.

[0060] Referring again to the data gathered by Paxson, as discussedabove, the data is well described by −⅓. Similarly, the data produced byCrovella, as also discussed above, β=⅓ for all file transfers and β=⅔for all text transfers accurately describes the observed data traffic.LAN traffic can also be accurately described or predicted by this modelas the data collected by Leland, as shown in FIGS. 2A-2B above, can bedescribed by an auto-correlation of β=⅓.

[0061] A computer network, such as computer network 300, can be viewedas having the following properties: First, the network 300 is aself-similar fractal network that can be described by a fractal patternand this fractal pattern is linear-preserving in the ports; Second, eachfinal branch (i.e., clients 302A-n) of the network 300 is throughputinvariant (i.e., passes approximately the same quantity of data); Third,that is also linear-preserving. One embodiment provides that networkdependant quantities scale as multiples of ⅓ power of the number ofnodes in the network 300.

Y=cNβ  Formula 8

[0062] Where Y is the load, c is a constant, N is the number of hosts(i.e., IP addresses or nodes) on the network, and β is a scalingexponent. The scaling exponent β is a multiple of ⅓. More generallystated, if the number of a number of nodes increases by a factor F, amore accurate prediction of the actual traffic load increase can bedescribed as F{circumflex over ( )}⅓. Therefore, as long as the CPUutilization on the servers is less than 1/F{circumflex over ( )}⅓,additional servers are not required to support the projected additionalload.

[0063] Referring again to FIG. 3 above, if device 320 is an OSI level 4device and receives packets from N nodes on the network. The device 320directs the traffic received to one or more servers 304A-n, depending onthe load. In one embodiment, the servers 304A-n are include loadbalancing capabilities such as using a round robin load balancingtechnique or any other load balancing method. If the number of nodes Ndouble (i.e., 2N), the load (Y) will only grow by 2{circumflex over( )}⅓, or a factor of about 1.26 as described in the above Formula 8.Therefore, if the existing servers can support about 126% of the currenttraffic load, then no additional servers are required. However, if theexisting servers cannot support 126% of the current traffic load, thenadditional servers may be required to balance the load.

[0064] In an alternative view, if the server CPU utilization is lessthan about 79% (the inverse of 1.26), then additional servers are notnecessary. However, if the CPU utilization is about 79% or greater, thenadditional servers are required to support the projected load caused bydoubling the number of nodes.

[0065]FIG. 4 is a flowchart of the method operations 400 of predictingnetwork data traffic according to one embodiment of the presentinvention. In operation 402, a first group of clients (e.g., clientnodes, nodes, etc) are coupled to a server. The demand from the firstgroup of clients results in a current or baseline CPU utilization of theserver.

[0066] In operation 404, a second group of clients are coupled to theserver. Coupling a first group of clients to the current server caninclude receiving a first group of requests from the first group ofclients. Coupling a second group of clients to the current server caninclude receiving a second group of requests from the second group ofclients.

[0067] In operation 406 a load multiple is determined. The load multiplecan be equal to a sum of the first group of clients and the second groupof clients divided by the first group of clients.

[0068] In operation 408 the current CPU utilization is compared to apredicted CPU utilization. The predicted CPU utilization can be equal toan inverse of a product of the first plurality of client nodes and theload multiple to a scaling exponent. The scaling exponent can be equalto a multiple of about ⅓. The scaling exponent can be equal to about ⅓.

[0069] In operation 410, a server requirement is increased if thecurrent CPU utilization is not greater than or equal to the predictedCPU utilization and the method operations end. Increasing the serverrequirement can include adding additional server CPU capacity such ascoupling additional servers (i.e., distributed server system) to thenetwork to meet the client's demands. Alternatively, the additionalproportions of server CPU can be reserved for meeting the demands of theclients. Adding additional server CPU capacity can also include addingadditional server CPU capacity until the current CPU utilization isgreater than the predicted CPU utilization. Increasing the serverrequirement can include outputting the predicted CPU utilization so thata network administrator/manager can be notified that the server is achoke point in the computer network design.

[0070] If, in operation 410, the current CPU utilization is greater thanor equal to the predicted CPU utilization, then no action is requiredand the method operations end.

[0071] While FIG. 4 describes an exemplary embodiment of identifying aserver as a choke point, it should be appreciated that similar methodsand operations can be utilized to identify any other portion orcomponent in the computer network that may be a choke point. Similarly,the systems and methods described herein can also be used in conjunctionwith computer network design and simulation software to identifypotential choke points and other design shortfalls.

[0072] As used herein the term “about” means ±10%. By way of example,the phrase “about 250” indicates a range of between 225 and 275.

[0073] Any of the operations described herein that form part of theinvention are useful machine operations. The invention also relates to adevice or an apparatus for performing these operations. The apparatusmay be specially constructed for the required purposes, or it may be ageneral-purpose computer selectively activated or configured by acomputer program stored in the computer. In particular, variousgeneral-purpose machines may be used with computer programs written inaccordance with the teachings herein, or it may be more convenient toconstruct a more specialized apparatus to perform the requiredoperations.

[0074] The invention can also be embodied as computer readable code on acomputer readable medium. The computer readable medium is any datastorage device that can store data, which can be thereafter, be read bya computer system. Examples of the computer readable medium include harddrives, network attached storage (NAS), read-only memory, random-accessmemory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical andnon-optical data storage devices. The computer readable medium can alsobe distributed over a network coupled computer systems so that thecomputer readable code is stored and executed in a distributed fashion.

[0075] Although the foregoing invention has been described in somedetail for purposes of clarity of understanding, it will be apparentthat certain changes and modifications may be practiced within the scopeof the appended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the invention is notto be limited to the details given herein, but may be modified withinthe scope and equivalents of the appended claims.

What is claimed is:
 1. A method of predicting network data trafficcomprising: coupling a first plurality of client nodes to a currentserver resulting in a current CPU utilization of the current server;coupling a second plurality of client nodes to the current server;determining a load multiple; comparing the current CPU utilization to apredicted CPU utilization; and increasing a server requirement if thecurrent CPU utilization is greater than or equal to the predicted CPUutilization.
 2. The method of claim 1, wherein the load multiple isequal to a sum of the first plurality of client nodes and the secondplurality of client nodes divided by the first plurality of clientnodes.
 3. The method of claim 1, wherein the predicted CPU utilizationequal to an inverse of a product of the first plurality of client nodesand the load multiple to a scaling exponent.
 4. The method of claim 3,wherein the scaling exponent is equal to a multiple of about ⅓.
 5. Themethod of claim 3, wherein the scaling exponent is equal to about ⅓. 6.The method of claim 1, wherein increasing the server requirementincludes adding additional server CPU capacity.
 7. The method of claim6, wherein adding additional server CPU capacity includes addingadditional server CPU capacity until the current CPU utilization isgreater than the predicted CPU utilization.
 8. The method of claim 1,wherein coupling a first plurality of client nodes to the current serverincludes receiving a first plurality of requests from the firstplurality of client nodes and wherein coupling a second plurality ofclient nodes to the current server includes receiving a second pluralityof requests from the second plurality of client nodes.
 9. The method ofclaim 1, wherein increasing the server requirement includes outputtingthe predicted CPU utilization.
 10. A method of predicting network datatraffic comprising: coupling a first plurality of client nodes to acurrent server; adding a second plurality of client nodes to the currentserver; determining a load multiple that is equal to a sum of the firstplurality of client nodes and the second plurality of client nodesdivided by the first plurality of client nodes; comparing a current CPUutilization of the current server to a predicted CPU utilization equalto an inverse of a product of the first plurality of client nodes andthe load multiple to a ⅓ power; and increasing a server requirement ifthe current CPU utilization is greater than or equal to the predictedCPU utilization.
 11. A system for managing network data trafficcomprising: a server system coupled to a computer network; a firstplurality of clients coupled to the network; a second plurality ofclients coupled to the network; and a load managing device coupled tothe network, the load managing device including: logic that determines acurrent CPU utilization of the server system; logic that determines aload multiple; logic that compares the current CPU utilization to apredicted CPU utilization; and logic that increases a server requirementif the current CPU utilization is greater than or equal to the predictedCPU utilization.
 12. The system of claim 11, wherein the load multipleis equal to a sum of the first plurality of clients and the secondplurality of clients divided by the first plurality of clients.
 13. Thesystem of claim 11, wherein the predicted CPU utilization equal to aninverse of a product of the first plurality of clients and the loadmultiple to a scaling exponent.
 14. The system of claim 13, wherein thescaling exponent is equal to a multiple of about ⅓.
 15. The system ofclaim 13, wherein the scaling exponent is equal to about ⅓.
 16. Thesystem of claim 11, wherein logic that increases the server requirementincludes logic that adds additional server CPU capacity.
 17. The systemof claim 16, wherein logic that increases the server requirementincludes logic that couples at least one additional server to thenetwork.
 18. The system of claim 16, wherein logic that adds additionalserver CPU capacity includes logic that adds additional server CPUcapacity until the current CPU utilization is greater than the predictedCPU utilization.
 19. The system of claim 11, wherein the load managingdevice includes: logic that receives a first plurality of requests fromthe first plurality of clients; and logic that receives a secondplurality of requests from the second plurality of client nodes.
 20. Thesystem of claim 11, wherein the logic that increases the serverrequirement includes logic that outputs the predicted CPU utilization.